Non-Intrusive Load Monitoring for High Power Consuming Appliances using Neural Networks
Author:
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9664652/9664656/09664681.pdf?arnumber=9664681
Reference8 articles.
1. Nonintrusive appliance load monitoring
2. Neural NILM
3. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
4. A new approach for event detection and feature extraction for NILM
5. Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
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1. NILM for Commercial Buildings: Deep Neural Networks Tackling Nonlinear and Multi-Phase Loads;Energies;2024-08-02
2. Maximizing Efficiency in Commercial Power Systems with an Optimized Load Classification and Identification Method Using Deep Learning and Ensemble Techniques;2023 IEEE World AI IoT Congress (AIIoT);2023-06-07
3. Deep Learning Based Non-Intrusive Load Monitoring for a Three-Phase System;IEEE Access;2023
4. Low-Cost Ensembling for Deep Neural Network based Non-Intrusive Load Monitoring;2022 IEEE World AI IoT Congress (AIIoT);2022-06-06
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